Scaling Hybrid Batch Training for Multilingual Retrieval Performance in mInstructor Models

Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual lang

Research goal: Does scaling the hybrid batch training strategy to larger multilingual models like mInstructor improve multilingual retrieval performance on the NQCross benchmark while preserving monolingual accuracy on MS-MARCO?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.